A framework to proactively consider road safety within the road planning process
Bibliographic record
Abstract
All too often, engineering strategies aimed at improving road safety are reactions to existing problems that occur after a road has been designed and built. Targeting problem locations and developing plans to reduce collisions are vital and have proven to be very successful. Transportation professionals, however, should also take a proactive approach to address road safety before problems emerge. This paper describes an evolving need of how to deal with road safety in a proactive manner. Although a proactive approach should improve the overall safety performance, there is currently a poor understanding of how to proactively plan for road safety. Several logistical and technical obstacles hinder the effective planning for road safety. Each of these obstacles is presented in detail, followed by a description of the opportunity to overcome each obstacle. The paper also includes the results of a case study used to demonstrate the proposed process. A proactive approach to road safety complements traditional, reactive methods currently in use. Significant progress will be realized once safety professionals shift their focus from fixing existing problems to helping plan roads that attempt to be problem free. The net result should be a safer road system.Key words: proactive road safety, safety audits, safety planning, safety evaluation, safety improvements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".